Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks
This addresses the challenge of managing polypharmacy risks for patients with multiple chronic conditions, though it is incremental as it applies existing methods to new medical data.
The study tackled the problem of unknown long-term effects of drug combinations on multimorbidity progression by querying tractable Bayesian network models learned from primary care data, confirming that prescriptions can lead to unintended negative consequences in cardiovascular disease multimorbidity and cross-group effects.
Managing patients with multimorbidity often results in polypharmacy: the prescription of multiple drugs. However, the long-term effects of specific combinations of drugs and diseases are typically unknown. In particular, drugs prescribed for one condition may result in adverse effects for the other. To investigate which types of drugs may affect the further progression of multimorbidity, we query models of diseases and prescriptions that are learned from primary care data. State-of-the-art tractable Bayesian network representations, on which such complex queries can be computed efficiently, are employed for these large medical networks. Our results confirm that prescriptions may lead to unintended negative consequences in further development of multimorbidity in cardiovascular diseases. Moreover, a drug treatment for one disease group may affect diseases of another group.